ATR-FTIR与机器学习相结合,用于快速非目标选新的精神活性物质
Yu Du1, Zhendong Hua2, Cuimei Liu2
1China Pharmaceutical University, Nanjing 210009, Jiangsu, PR China.
Forensic science international
|June 16, 2023
概括
机器学习模型使用FTIR光谱学准确地分类新的精神活性物质 (NPS). 这些模型可以快速地在现场选多样化和不断变化的NPS,提高公共安全.
科学领域:
- 分析化学 分析化学
- 法医科学 法医科学 法医科学
- 计算化学计算化学
背景情况:
- 新型精神活性物质 (NPS) 由于其快速演变和多样性,对全球公共健康和安全构成重大风险.
- 使用减弱总反射-里埃变换红外光谱法 (ATR-FTIR) 的传统向选方法与NPS的快速结构修改作斗争.
- 需要快速,非目标查方法,能够在没有先前参考数据的情况下识别新的NPS.
研究的目的:
- 开发和验证机器学习 (ML) 模型,以便使用FTIR光谱学快速,非目标地对各种NPS类别进行分类.
- 研究合成大麻素中的结构光谱性质关系,以便分类分类.
- 创建适用于桌面和便携式FTIR光谱仪的ML模型,用于现场NPS查.
主要方法:
- 使用桌面和便携式ATR-FTIR光谱仪从362个NPS类型中收集了1099个IR光谱.
- 开发和交叉验证了六个ML分类模型 (KNN,SVM,RF,ET,投票,ANN) 来分类八个NPS类.
- 用合成大麻素的层次集群分析 (HCA) 识别结构-光谱关系并定义子类别,然后为这些子类别开发ML模型.
主要成果:
- 在NPS类别的六个ML模型中,通过从0.87到1.00的f1得分实现了高分类性能.
- 根据结构光谱特性确定了八个不同的合成大麻素子类别.
- 通过使用不同FTIR仪器的数据,证明了ML模型的成功应用,用于分类NPS主要类别和合成大麻素子类别.
结论:
- 开发的ML模型适用于桌面和便携式FTIR光谱仪,可快速,准确和经济高效地对新兴NPS进行现场查.
- 这些模型为法医实验室和公共卫生机构提供了宝贵的工具,以应对不断变化的NPS环境所带来的挑战.
- 该研究为非目标NPS查建立了一个框架,即使参考数据无法获得,也可以显著提高检测能力.
关键词:
减弱的总反射 - 里埃变换红外光谱学 (ATR-FTIR)分类 分类 分类 分类.阶层集群分析 (HCA)机器学习 (ML) 是指机器学习.新型精神活性物质 (NPS) 是一种新型精神活性物质.非有针对性的查.更多相关视频
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